We use modern and scalable Open Source technologies. Our solutions benefit from the largest developer community worldwide and enable you to use project results easily and without dependence on black box solutions.

We use modern and scalable Open Source technologies. Our solutions benefit from the largest developer community worldwide and enable you to use project results easily and without dependence on black box solutions.

Fintu Data Science implemented a tailor-made machine learning solution for us in a very short time. We were extremely impressed with the project results, Fintu's know-how and the valuable exchange of experience with our in-house data scientists.

Jonathan Überall, Head of Data Intelligence, 1&1 Versatel GmbH

The pricing solution developed by Fintu Data Science enables us to offer the apartments of our customers at the best price from the very beginning. The work with Fintu has been excellent and their expertise has been extremely helpful.

Julian Ritter, CEO, Airgreets GmbH

What our customers say

Fintu Data Science implemented a tailor-made machine learning solution for us in a very short time. We were extremely impressed with the project results, Fintu's know-how and the valuable exchange of experience with our in-house data scientists.

Jonathan Überall, Head of Data Intelligence, 1&1 Versatel GmbH

The pricing solution developed by Fintu Data Science enables us to offer the apartments of our customers at the best price from the very beginning. The work with Fintu has been excellent and their expertise has been extremely helpful.

Eliminate duplicates and link different data sources with artificial intelligence.

Do duplicates and poor data quality hinder your processes? Do you want to associate objects from internal databases, business partners or external sources - despite different spellings, typos and missing entries?

Our proprietary machine learning algorithms for data matching are more accurate and faster than traditional master data management solutions and do not require manual rule creation. Save yourself time-consuming work and let artificial intelligence do the work for you.

Sales staff spend more than 50% of their time working on the wrong potentials. Artificial intelligence can drastically reduce this number.

Our Lead Prediction Engine links your customer data to external databases to identify the most promising leads. Your salespeople will get the leads with the highest probability of closing and the highest revenue potential, allowing them to focus on what they do best - selling.

The optimal price for your products depends on many factors. With a machine learning model the most important factors can be determined and a precise price optimization can be carried out.

The current market prices are determined by an automated analysis of web data. A machine learning model then learns the optimal price points for your products based on market data and sales data. The prediction of the best price becomes more accurate over time through feedback from the current sales figures and adapts to new circumstances (for example, a price change by a competitor).

With the right machine learning methods, the migration of a customer can be predicted in time to initiate retention measures.

Based on historical customer data, a machine learning model is trained to recognize identifying signs for churn and report vulnerable customers. This allows retention measures to be initiated in time to retain the customer.

When analyzing churn events, it is important to understand the customer history in its actual sequence, not as a collection of independent data points. We use LSTM deep learning models to analyze customer history as a time series and achieve better results than traditional machine learning models.

Increase your cross-selling success with targeted suggestions for next products and the right time to address them.

Which products a customer will most likely buy next can often be predicted with a recommendation engine. Targeted advertising and cross-selling campaigns can maximize existing customer sales.

Based on the product history of current customers, frequent product combinations and sequences are identified. For each existing customer, product recommendations can then be given in real time and an optimal moment for the offer can be suggested.

A predictive model for failures of production machines and other equipment enables on demand and planned maintenance based on sensor data.

Using the example of earlier failures, a machine learning model is trained to recognize the most important warning signals - even in "background noise" of sensor data. By regularly checking or monitoring measurement data live, possible failures can be detected in advance and prevented by targeted maintenance.

Reduce inventory costs and delivery times with accurate demand forecasting.

With a more accurate forecast of future demand for products, inventory costs and delivery times can be optimized. A machine learning model also learns complex correlations of influencing factors and can thus deliver a more accurate forecast than conventional methods.

For this purpose, historical data are enriched with possible external influencing factors (e.g. weather data) and seasonalities are modelled. A machine learning model is then trained to predict future demand for individual products or product groups.

Credit defaults, fraudulent insurance claims and bad debt from contracts can cause significant costs and revenue losses. Artificial intelligence can help where classic creditworthiness information about business partners is not sufficient or individual transactions have to be checked.

A machine learning model is trained on the basis of a few hand-classified transactions. In addition to customer profiles, external data points, such as social media activities, can also be included as additional influencing factors. The model learns to distinguish fraudulent activities from normal activities and can also be used for real-time scoring, for example when concluding a contract.

Eliminate duplicates and link different data sources with artificial intelligence.

Do duplicates and poor data quality hinder your processes? Do you want to associate objects from internal databases, business partners or external sources - despite different spellings, typos and missing entries?

Our proprietary machine learning algorithms for data matching are more accurate and faster than traditional master data management solutions and do not require manual rule creation. Save yourself time-consuming work and let artificial intelligence do the work for you.

Sales staff spend more than 50% of their time working on the wrong potentials. Artificial intelligence can drastically reduce this number.

Our Lead Prediction Engine links your customer data to external databases to identify the most promising leads. Your salespeople will get the leads with the highest probability of closing and the highest revenue potential, allowing them to focus on what they do best - selling.

The optimal price for your products depends on many factors. With a machine learning model the most important factors can be determined and a precise price optimization can be carried out.

The current market prices are determined by an automated analysis of web data. A machine learning model then learns the optimal price points for your products based on market data and sales data. The prediction of the best price becomes more accurate over time through feedback from the current sales figures and adapts to new circumstances (for example, a price change by a competitor).

With the right machine learning methods, the migration of a customer can be predicted in time to initiate retention measures.

Based on historical customer data, a machine learning model is trained to recognize identifying signs for churn and report vulnerable customers. This allows retention measures to be initiated in time to retain the customer.

When analyzing churn events, it is important to understand the customer history in its actual sequence, not as a collection of independent data points. We use LSTM deep learning models to analyze customer history as a time series and achieve better results than traditional machine learning models.

Increase your cross-selling success with targeted suggestions for next products and the right time to address them.

Which products a customer will most likely buy next can often be predicted with a recommendation engine. Targeted advertising and cross-selling campaigns can maximize existing customer sales.

Based on the product history of current customers, frequent product combinations and sequences are identified. For each existing customer, product recommendations can then be given in real time and an optimal moment for the offer can be suggested.

A predictive model for failures of production machines and other equipment enables on demand and planned maintenance based on sensor data.

Using the example of earlier failures, a machine learning model is trained to recognize the most important warning signals - even in "background noise" of sensor data. By regularly checking or monitoring measurement data live, possible failures can be detected in advance and prevented by targeted maintenance.

Reduce inventory costs and delivery times with accurate demand forecasting.

With a more accurate forecast of future demand for products, inventory costs and delivery times can be optimized. A machine learning model also learns complex correlations of influencing factors and can thus deliver a more accurate forecast than conventional methods.

For this purpose, historical data are enriched with possible external influencing factors (e.g. weather data) and seasonalities are modelled. A machine learning model is then trained to predict future demand for individual products or product groups.

Credit defaults, fraudulent insurance claims and bad debt from contracts can cause significant costs and revenue losses. Artificial intelligence can help where classic creditworthiness information about business partners is not sufficient or individual transactions have to be checked.

A machine learning model is trained on the basis of a few hand-classified transactions. In addition to customer profiles, external data points, such as social media activities, can also be included as additional influencing factors. The model learns to distinguish fraudulent activities from normal activities and can also be used for real-time scoring, for example when concluding a contract.